Detection of dominant QTLs for stigma exsertion ratio in rice derived from <i>Oryza rufipogon</i> accession ‘W0120’
Bibliographic record
Abstract
Stigma exsertion can enhance the outcrossing fertility in rice. Dominant genes for this trait are expected to be essential for the effective application of a novel outcrossing-based breeding system that uses male sterility in rice, which is normally autogamous. Because reduction of stigma exsertion is a domestication trait, we screened wild rice species as possible donors of genes or QTLs for stigma exsertion. We used in silico image-based screening and selected the Oryza rufipogon accession ‘W0120’. A single F1 individual derived from a cross between the japonica rice cultivar ‘Akidawara’ and ‘W0120’ was used to generate F2 and BC1F1 populations. QTL analysis performed using 114 F2 individuals detected QTLs on chromosomes 2, 3, 4, 8, and 11. Only two major QTLs on chromosomes 3 and 8 showed higher degrees of dominance. On the other hand, there were no QTLs near GS3, which is well known as a gene for stigma exsertion. Validation of these QTLs using 188 BC1F1 individuals provided clear evidence for their dominance. Genotypes of the markers nearest to the two QTLs were also related to grain length. We expect the genes responsible for these QTLs to be promising tools for improving outcrossing-based breeding in rice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".